• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.1
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• pp.73-83
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• 2013

In previous works, psychoacoustic parameters have been used for objective quantification. However, these parameters do not agree well with subjective assessment. Therefore, the correlation between psychoacoustic parameters and the subjective rating of door closing sounds of sampled cars is low, and it is not sufficient to use psychoacoustic parameters as an objective metric to quantify the sound quality of door closing sounds. In this paper, a new method is proposed to objectively quantify the sound quality based on physiological acoustics and statistical signal processing. The gammatone filter, as a pre-processing, is used in models of the auditory system and kurtosis, which is the fourth-order moment of temporal signal, and is used to extract information about sound quality quantification for door closing sounds. The new metric obtained through the proposed method is highly correlated with subjective rating, and it is successfully applied to the quantification of the sound quality of door closing sounds.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.212-217
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• 2007

As a preliminary study for the development of electrical stimulation strategy of artificial retina, we set up a method fur the reconstruction of input intensity variation from retinal ganglion cell(RGC) responses. In order to estimate light intensity variation, we used an optimal linear filter trained from given stimulus intensity variation and multiple single unit spike trains from RGCs. By applying ON/OFF stimulation(ON duration: 2 sec, OFF duration: 5 sec) repetitively, we identified three functional types of ganglion cells according to when they respond to the ON/OFF stimulus actively: ON cell, OFF cell, and ON-OFF cell. Experiments were also performed using a Gaussian random stimulus and a binary random stimulus. The input intensity was updated once every 90 msec(i. e. 11 Hz) to present the stimulus. The result of reconstructing 11 Hz Gaussian and binary random stimulus was not satisfactory and showed low correlation between the original and reconstructed stimulus. In the case of ON/OFF stimulus in which temporal variation is slow, successful reconstruction was achieved and the correlation coefficient was as high as 0.8.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.371-377
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• 2013

This paper proposes three methods for improving the accuracy of auto-music transcription considering with time-varying tempo from monophonic sound. The first one that uses TV(Total Variation) filter for smoothing the pitch data reduces the fragmentation in the pitch segmentation result. Also, the measure finding method that combines three different ways based on pitch and energy of sound data, respectively as well as based on rules produces more stable result. In addition the temporal result of note-length encoding is corrected in optimal way that the resulted encoding minimizes the sum of quantization error in a measure while the sum of note-lengths is equal to the number of beats. In the experiment with 16 children songs, we obtained the improved result in which measure finding was complete, the accuracy of encoding for note-length and pitch was about 91.3 and 86.7, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.392-402
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• 2010

Black carbon (BC) concentrations were measured with an aethalometer (AE-16, 880 nm) at time interval of 5-min at an urban site of Gwangju over a year 2008. 24-hr filter-based integrated measurements of $PM_{2.5}$ particles were also made at the same site during the winter and summer intensive periods to test any optical loading bias in the raw BC data measured by aethalometer. BC concentration was higher in winter than in summer, possibly due to increase in emissions from energy consumption and poor dispersion with reduction of boundary layer in winter. Also temporal cycles of BC indicate that short-term transient spikes were common, occurring primarily during the rush-hour periods. A similar feature was also observed in diurnal concentration cycle of CO, mainly emitted from motor vehicles. When both low wind speed and weather patterns such as mist, haze and etc were combined, high BC concentrations frequently occurred. The amount of optical loading effect described by the "k" factor showed the seasonal variation, ranging from 0.0003 to 0.0036. This implies that optical loading effect is not seen at all times. From the comparison between the filter-based elemental carbon (EC) and aethalometer BC data, it was found that the loading compensated BC values were more reasonable than the raw BC ones reported from the aethalometer.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.65-70
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• 2000

It is blown that the loudness is one of the most important metrics in assessing the sound quality and a calculation method for loudness has been standardized for steady sounds. In this study, a new loudness model is suggested for dealing with the transient sound for a unified analysis of various practical sounds. A signal processing technique is introduced for this purpose, which is required for the band subdivision and the prediction of band-level change of transient sounds. In addition, models for the post-masking and the temporal integration are adopted in the analysis of the loudness of transient sounds. In order to solve the problem of the conventional loudness model in the pure-tone signal processing, a critical band filter is employed in the analysis, which consists of 47 critical filters having a filter spacing of a half of the critical bandwidth. For testing the effectiveness of the present model, the predicted responses are compared with the experimental data and it is observed that they are in good agreements.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.29-39
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• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.209-219
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• 2019

Satellite imagery occurs geometric and radiometric errors due to external environmental factors at the acquired time, which in turn causes false-alarm in change detection. These errors should be eliminated by geometric and radiometric corrections. In this study, we propose a methodology that automatically and simultaneously performs geometric and radiometric corrections by using the SURF (Speeded-Up Robust Feature) algorithm and the mask filter. The MPs (Matching Points), which show invariant properties between multi-temporal imagery, extracted through the SURF algorithm are used for automatic geometric correction. Using the properties of the extracted MPs, PIFs (Pseudo Invariant Features) used for relative radiometric correction are selected. Subsequently, secondary PIFs are extracted by generated mask filters around the selected PIFs. After performing automatic using the extracted MPs, we could confirm that geometric and radiometric errors are eliminated as the result of performing the relative radiometric correction using PIFs in geo-rectified images.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1563-1571
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• 2009

In this paper, we propose a new boundary matching method for the temporal error concealment and a post processing algorithm for perceptual quality improvement of the concealed frame. Temporal error concealment is a method that substitutes error blocks with similar blocks from the reference frame. In conventional H.264/AVC standard, it compares outside pixels of erroneous block with inside pixels of reference block to find the most similar block. However, it is very possible that the conventional method substitutes erroneous block with the wrong one because it compares only narrow spatial range of pixels. In this paper, for substituting erroneous blocks with more correct blocks, we propose enhanced boundary matching method by comparing inside and outside pixels of reference block with outside pixels of erroneous block and setting up additional candidate motion vector in the fixed search range based on maximum and minimum value of candidate motion vectors. Furthermore, we propose a post processing method to smooth edges between concealed and decoded blocks without error by using the modified deblocking filter. We identified that the proposed method shows quality improvement of about 0.9dB over the conventional boundary matching methods.

• ETRI Journal
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• v.29 no.3
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• pp.300-304
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• 2007

This paper proposes a new data-driven method for high-pass approaches, which suppresses slow-varying noise components. Conventional high-pass approaches are based on the idea of decorrelating the feature vector sequence, and are trying for adaptability to various conditions. The proposed method is based on temporal local decorrelation using the information-maximization theory for each utterance. This is performed on an utterance-by-utterance basis, which provides an adaptive channel normalization filter for each condition. The performance of the proposed method is evaluated by isolated-word recognition experiments with channel distortion. Experimental results show that the proposed method yields outstanding improvement for channel-distorted speech recognition.